class: center, middle, inverse, title-slide .title[ # Three common mistakes in statistics and how to avoid them ] .author[ ### Elizabeth Pankratz ] .institute[ ### Department of Psychology<br>The University of Edinburgh ] --- ### Something you won’t be able to unsee -- .center[] -- .center[Taking the means of discrete ratings is very common—but a little strange!] --- ### Why Likert scale ratings aren’t continuous numeric -- .center[  ] ??? Define Likert Scale "Likert" v "Lickert" --- count:false ### Why Likert scale ratings aren’t continuous numeric .center[  ] --- count:false ### Why Likert scale ratings aren’t continuous numeric .center[  ] --- count:false ### Why Likert scale ratings aren’t continuous numeric .center[  ] --- count:false ### Why Likert scale ratings aren’t continuous numeric .center[  ] -- .center[**Numbers on a Likert scale are just labels.**] --- count:false ### Why Likert scale ratings aren’t continuous numeric .center[  ] .center[**Numbers on a Likert scale are just labels.**] --- count:false ### Why Likert scale ratings aren’t continuous numeric .center[  ] .center[**Numbers on a Likert scale are just labels.**] --- .pull-left[ ### The mistake ] .pull-right[ ### How you’ll avoid it ] -- .pull-left[ <svg aria-hidden="true" role="img" viewBox="0 0 640 512" style="height:1em;width:1.25em;vertical-align:-0.125em;margin-left:auto;margin-right:0.2em;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M392.8 1.2c-17-4.9-34.7 5-39.6 22l-128 448c-4.9 17 5 34.7 22 39.6s34.7-5 39.6-22l128-448c4.9-17-5-34.7-22-39.6zm80.6 120.1c-12.5 12.5-12.5 32.8 0 45.3L562.7 256l-89.4 89.4c-12.5 12.5-12.5 32.8 0 45.3s32.8 12.5 45.3 0l112-112c12.5-12.5 12.5-32.8 0-45.3l-112-112c-12.5-12.5-32.8-12.5-45.3 0zm-306.7 0c-12.5-12.5-32.8-12.5-45.3 0l-112 112c-12.5 12.5-12.5 32.8 0 45.3l112 112c12.5 12.5 32.8 12.5 45.3 0s12.5-32.8 0-45.3L77.3 256l89.4-89.4c12.5-12.5 12.5-32.8 0-45.3z"/></svg> **A common R mistake:** Letting R treat all variables that look like numbers as continuous numeric. ] .pull-right[ ] -- .pull-left[ <svg aria-hidden="true" role="img" viewBox="0 0 448 512" style="height:1em;width:0.88em;vertical-align:-0.125em;margin-left:auto;margin-right:0.2em;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M160 80c0-26.5 21.5-48 48-48h32c26.5 0 48 21.5 48 48V432c0 26.5-21.5 48-48 48H208c-26.5 0-48-21.5-48-48V80zM0 272c0-26.5 21.5-48 48-48H80c26.5 0 48 21.5 48 48V432c0 26.5-21.5 48-48 48H48c-26.5 0-48-21.5-48-48V272zM368 96h32c26.5 0 48 21.5 48 48V432c0 26.5-21.5 48-48 48H368c-26.5 0-48-21.5-48-48V144c0-26.5 21.5-48 48-48z"/></svg> **An advanced stats mistake:** Modelling categorical, ordinal data as if it were numeric. ] .pull-right[ ] -- .pull-left[ <svg aria-hidden="true" role="img" viewBox="0 0 384 512" style="height:1em;width:0.75em;vertical-align:-0.125em;margin-left:auto;margin-right:0.2em;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M192 32c17.7 0 32 14.3 32 32V199.5l111.5-66.9c15.2-9.1 34.8-4.2 43.9 11s4.2 34.8-11 43.9L254.2 256l114.3 68.6c15.2 9.1 20.1 28.7 11 43.9s-28.7 20.1-43.9 11L224 312.5V448c0 17.7-14.3 32-32 32s-32-14.3-32-32V312.5L48.5 379.4c-15.2 9.1-34.8 4.2-43.9-11s-4.2-34.8 11-43.9L129.8 256 15.5 187.4c-15.2-9.1-20.1-28.7-11-43.9s28.7-20.1 43.9-11L160 199.5V64c0-17.7 14.3-32 32-32z"/></svg> **A foundational stats mistake:** Interpreting a significant *p*-value as evidence that an effect exists in the real world. ] .pull-right[ ] ??? Say that we're going thru the life cycle of data analysis, starting with data prep and ending with hypothesis testing. --- ### The data: Students’ anxiety ratings for “Going to ask my statistics teacher for individual help with material I am having difficulty understanding”. -- <img src="data:image/png;base64,#demo_files/figure-html/bar-aggregated-1.png" width="576" style="display: block; margin: auto;" /> --- ### The data: Students’ anxiety ratings for “Going to ask my statistics teacher for individual help with material I am having difficulty understanding”. .pull-left[ ``` r slice(anx, 45:50) ``` ``` ## # A tibble: 6 × 3 ## unique_id gender rating ## <chr> <chr> <dbl> ## 1 7d28c303 Female/Woman 4 ## 2 7d55383a Another Gender 4 ## 3 8116550a Female/Woman 1 ## 4 83491ff9 Female/Woman 4 ## 5 8450f8ad Male/Man 2 ## 6 876547d6 Female/Woman 3 ``` ] -- .pull-right[ `rating` looks like numbers, and R treats it like numbers, as `dbl`. So it’s tempting to manipulate it like numbers. ``` r mean(anx$rating) ``` ``` ## [1] 2.868054 ``` ] --- ### Remember: We are smarter than R is -- Store categorical variables as factors. ``` r anx <- anx |> mutate(rating = factor(rating)) ``` <br> -- Now it’s impossible to incorrectly treat them as if they're numeric! ``` r mean(anx$rating) ``` ``` ## [1] NA ``` --- .pull-left[ ### The mistake ] .pull-right[ ### How you’ll avoid it ] .pull-left[ <svg aria-hidden="true" role="img" viewBox="0 0 640 512" style="height:1em;width:1.25em;vertical-align:-0.125em;margin-left:auto;margin-right:0.2em;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M392.8 1.2c-17-4.9-34.7 5-39.6 22l-128 448c-4.9 17 5 34.7 22 39.6s34.7-5 39.6-22l128-448c4.9-17-5-34.7-22-39.6zm80.6 120.1c-12.5 12.5-12.5 32.8 0 45.3L562.7 256l-89.4 89.4c-12.5 12.5-12.5 32.8 0 45.3s32.8 12.5 45.3 0l112-112c12.5-12.5 12.5-32.8 0-45.3l-112-112c-12.5-12.5-32.8-12.5-45.3 0zm-306.7 0c-12.5-12.5-32.8-12.5-45.3 0l-112 112c-12.5 12.5-12.5 32.8 0 45.3l112 112c12.5 12.5 32.8 12.5 45.3 0s12.5-32.8 0-45.3L77.3 256l89.4-89.4c12.5-12.5 12.5-32.8 0-45.3z"/></svg> **A common R mistake:** Letting R treat all variables that look like numbers as continuous numeric. ] .pull-right[ ] .pull-left[ <svg aria-hidden="true" role="img" viewBox="0 0 448 512" style="height:1em;width:0.88em;vertical-align:-0.125em;margin-left:auto;margin-right:0.2em;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M160 80c0-26.5 21.5-48 48-48h32c26.5 0 48 21.5 48 48V432c0 26.5-21.5 48-48 48H208c-26.5 0-48-21.5-48-48V80zM0 272c0-26.5 21.5-48 48-48H80c26.5 0 48 21.5 48 48V432c0 26.5-21.5 48-48 48H48c-26.5 0-48-21.5-48-48V272zM368 96h32c26.5 0 48 21.5 48 48V432c0 26.5-21.5 48-48 48H368c-26.5 0-48-21.5-48-48V144c0-26.5 21.5-48 48-48z"/></svg> **An advanced stats mistake:** Modelling categorical, ordinal data as if it were numeric. ] .pull-right[ ] .pull-left[ <svg aria-hidden="true" role="img" viewBox="0 0 384 512" style="height:1em;width:0.75em;vertical-align:-0.125em;margin-left:auto;margin-right:0.2em;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M192 32c17.7 0 32 14.3 32 32V199.5l111.5-66.9c15.2-9.1 34.8-4.2 43.9 11s4.2 34.8-11 43.9L254.2 256l114.3 68.6c15.2 9.1 20.1 28.7 11 43.9s-28.7 20.1-43.9 11L224 312.5V448c0 17.7-14.3 32-32 32s-32-14.3-32-32V312.5L48.5 379.4c-15.2 9.1-34.8 4.2-43.9-11s-4.2-34.8 11-43.9L129.8 256 15.5 187.4c-15.2-9.1-20.1-28.7-11-43.9s28.7-20.1 43.9-11L160 199.5V64c0-17.7 14.3-32 32-32z"/></svg> **A foundational stats mistake:** Interpreting a significant *p*-value as evidence that an effect exists in the real world. ] .pull-right[ ] --- count:false .pull-left[ ### The mistake ] .pull-right[ ### How you’ll avoid it ] .pull-left[ <svg aria-hidden="true" role="img" viewBox="0 0 640 512" style="height:1em;width:1.25em;vertical-align:-0.125em;margin-left:auto;margin-right:0.2em;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M392.8 1.2c-17-4.9-34.7 5-39.6 22l-128 448c-4.9 17 5 34.7 22 39.6s34.7-5 39.6-22l128-448c4.9-17-5-34.7-22-39.6zm80.6 120.1c-12.5 12.5-12.5 32.8 0 45.3L562.7 256l-89.4 89.4c-12.5 12.5-12.5 32.8 0 45.3s32.8 12.5 45.3 0l112-112c12.5-12.5 12.5-32.8 0-45.3l-112-112c-12.5-12.5-32.8-12.5-45.3 0zm-306.7 0c-12.5-12.5-32.8-12.5-45.3 0l-112 112c-12.5 12.5-12.5 32.8 0 45.3l112 112c12.5 12.5 32.8 12.5 45.3 0s12.5-32.8 0-45.3L77.3 256l89.4-89.4c12.5-12.5 12.5-32.8 0-45.3z"/></svg> **A common R mistake:** Letting R treat all variables that look like numbers as continuous numeric. ] .pull-right[ When a variable comes from a Likert scale, tell R it’s categorical using `factor()`. ] .pull-left[ <svg aria-hidden="true" role="img" viewBox="0 0 448 512" style="height:1em;width:0.88em;vertical-align:-0.125em;margin-left:auto;margin-right:0.2em;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M160 80c0-26.5 21.5-48 48-48h32c26.5 0 48 21.5 48 48V432c0 26.5-21.5 48-48 48H208c-26.5 0-48-21.5-48-48V80zM0 272c0-26.5 21.5-48 48-48H80c26.5 0 48 21.5 48 48V432c0 26.5-21.5 48-48 48H48c-26.5 0-48-21.5-48-48V272zM368 96h32c26.5 0 48 21.5 48 48V432c0 26.5-21.5 48-48 48H368c-26.5 0-48-21.5-48-48V144c0-26.5 21.5-48 48-48z"/></svg> **An advanced stats mistake:** Modelling categorical, ordinal data as if it were numeric. ] .pull-right[ ] .pull-left[ <svg aria-hidden="true" role="img" viewBox="0 0 384 512" style="height:1em;width:0.75em;vertical-align:-0.125em;margin-left:auto;margin-right:0.2em;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M192 32c17.7 0 32 14.3 32 32V199.5l111.5-66.9c15.2-9.1 34.8-4.2 43.9 11s4.2 34.8-11 43.9L254.2 256l114.3 68.6c15.2 9.1 20.1 28.7 11 43.9s-28.7 20.1-43.9 11L224 312.5V448c0 17.7-14.3 32-32 32s-32-14.3-32-32V312.5L48.5 379.4c-15.2 9.1-34.8 4.2-43.9-11s-4.2-34.8 11-43.9L129.8 256 15.5 187.4c-15.2-9.1-20.1-28.7-11-43.9s28.7-20.1 43.9-11L160 199.5V64c0-17.7 14.3-32 32-32z"/></svg> **A foundational stats mistake:** Interpreting a significant *p*-value as evidence that an effect exists in the real world. ] .pull-right[ ] --- ### What ordinal regression models do -- <img src="data:image/png;base64,#demo_files/figure-html/plot-underlying-normal-0-1.png" width="864" style="display: block; margin: auto;" /> --- count: false ### What ordinal regression models do <img src="data:image/png;base64,#demo_files/figure-html/plot-underlying-normal-1.png" width="864" style="display: block; margin: auto;" /> --- count: false ### What ordinal regression models do <img src="data:image/png;base64,#demo_files/figure-html/plot-underlying-normal2-1.png" width="864" style="display: block; margin: auto;" /> --- ### Fit ordinal regression models with `polr()` -- ``` r library(MASS) # MASS contains the polr() function anx_fit1 <- polr( rating ~ 1, # intercept-only model, to start data = anx, Hess = TRUE, method = 'probit' # ask me in the Q+A! ) ``` --- ### Fit ordinal regression models with `polr()` .pull-left[ ``` r summary(anx_fit1) ```  ] -- .pull-right[ <img src="data:image/png;base64,#demo_files/figure-html/plot-underlying-normal-side1-1.png" width="432" style="display: block; margin: auto;" /> ] --- count: false ### Fit ordinal regression models with `polr()` .pull-left[ ``` r summary(anx_fit1) ```  ] .pull-right[ <img src="data:image/png;base64,#demo_files/figure-html/plot-underlying-normal-side2-1.png" width="432" style="display: block; margin: auto;" /> ] --- #### How does a student’s gender affect ratings for “Going to ask my statistics teacher for individual help with material I am having difficulty understanding”? -- .pull-left[ <img src="data:image/png;base64,#demo_files/figure-html/plot-gender-bars-1.png" width="504" style="display: block; margin: auto;" /> ] -- .pull-right[ <img src="data:image/png;base64,#demo_files/figure-html/normals-stacked-onlyfem-1.png" width="504" style="display: block; margin: auto;" /> ] --- .center[ <img src="data:image/png;base64,#demo_files/figure-html/fem-normal-solo-1.png" width="936" style="display: block; margin: auto;" /> ] --- count: false <img src="data:image/png;base64,#demo_files/figure-html/fem-mal-normals-1.png" width="936" style="display: block; margin: auto;" /> --- count: false <img src="data:image/png;base64,#demo_files/figure-html/all-gender-normals-1.png" width="936" style="display: block; margin: auto;" /> --- .pull-left[ ### The mistake ] .pull-right[ ### How you’ll avoid it ] <!-- --> .pull-left[ <svg aria-hidden="true" role="img" viewBox="0 0 640 512" style="height:1em;width:1.25em;vertical-align:-0.125em;margin-left:auto;margin-right:0.2em;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M392.8 1.2c-17-4.9-34.7 5-39.6 22l-128 448c-4.9 17 5 34.7 22 39.6s34.7-5 39.6-22l128-448c4.9-17-5-34.7-22-39.6zm80.6 120.1c-12.5 12.5-12.5 32.8 0 45.3L562.7 256l-89.4 89.4c-12.5 12.5-12.5 32.8 0 45.3s32.8 12.5 45.3 0l112-112c12.5-12.5 12.5-32.8 0-45.3l-112-112c-12.5-12.5-32.8-12.5-45.3 0zm-306.7 0c-12.5-12.5-32.8-12.5-45.3 0l-112 112c-12.5 12.5-12.5 32.8 0 45.3l112 112c12.5 12.5 32.8 12.5 45.3 0s12.5-32.8 0-45.3L77.3 256l89.4-89.4c12.5-12.5 12.5-32.8 0-45.3z"/></svg> **A common R mistake:** Letting R treat all variables that look like numbers as continuous numeric. ] .pull-right[ When a variable comes from a Likert scale, tell R it’s categorical using `factor()`. ] <!-- --> .pull-left[ <svg aria-hidden="true" role="img" viewBox="0 0 448 512" style="height:1em;width:0.88em;vertical-align:-0.125em;margin-left:auto;margin-right:0.2em;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M160 80c0-26.5 21.5-48 48-48h32c26.5 0 48 21.5 48 48V432c0 26.5-21.5 48-48 48H208c-26.5 0-48-21.5-48-48V80zM0 272c0-26.5 21.5-48 48-48H80c26.5 0 48 21.5 48 48V432c0 26.5-21.5 48-48 48H48c-26.5 0-48-21.5-48-48V272zM368 96h32c26.5 0 48 21.5 48 48V432c0 26.5-21.5 48-48 48H368c-26.5 0-48-21.5-48-48V144c0-26.5 21.5-48 48-48z"/></svg> **An advanced stats mistake:** Modelling categorical, ordinal data as if it were numeric. ] .pull-right[ ] <!-- --> .pull-left[ <svg aria-hidden="true" role="img" viewBox="0 0 384 512" style="height:1em;width:0.75em;vertical-align:-0.125em;margin-left:auto;margin-right:0.2em;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M192 32c17.7 0 32 14.3 32 32V199.5l111.5-66.9c15.2-9.1 34.8-4.2 43.9 11s4.2 34.8-11 43.9L254.2 256l114.3 68.6c15.2 9.1 20.1 28.7 11 43.9s-28.7 20.1-43.9 11L224 312.5V448c0 17.7-14.3 32-32 32s-32-14.3-32-32V312.5L48.5 379.4c-15.2 9.1-34.8 4.2-43.9-11s-4.2-34.8 11-43.9L129.8 256 15.5 187.4c-15.2-9.1-20.1-28.7-11-43.9s28.7-20.1 43.9-11L160 199.5V64c0-17.7 14.3-32 32-32z"/></svg> **A foundational stats mistake:** Interpreting a significant *p*-value as evidence that an effect exists in the real world. ] .pull-right[ ] --- count: false .pull-left[ ### The mistake ] .pull-right[ ### How you’ll avoid it ] <!-- --> .pull-left[ <svg aria-hidden="true" role="img" viewBox="0 0 640 512" style="height:1em;width:1.25em;vertical-align:-0.125em;margin-left:auto;margin-right:0.2em;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M392.8 1.2c-17-4.9-34.7 5-39.6 22l-128 448c-4.9 17 5 34.7 22 39.6s34.7-5 39.6-22l128-448c4.9-17-5-34.7-22-39.6zm80.6 120.1c-12.5 12.5-12.5 32.8 0 45.3L562.7 256l-89.4 89.4c-12.5 12.5-12.5 32.8 0 45.3s32.8 12.5 45.3 0l112-112c12.5-12.5 12.5-32.8 0-45.3l-112-112c-12.5-12.5-32.8-12.5-45.3 0zm-306.7 0c-12.5-12.5-32.8-12.5-45.3 0l-112 112c-12.5 12.5-12.5 32.8 0 45.3l112 112c12.5 12.5 32.8 12.5 45.3 0s12.5-32.8 0-45.3L77.3 256l89.4-89.4c12.5-12.5 12.5-32.8 0-45.3z"/></svg> **A common R mistake:** Letting R treat all variables that look like numbers as continuous numeric. ] .pull-right[ When a variable comes from a Likert scale, tell R it’s categorical using `factor()`. ] <!-- --> .pull-left[ <svg aria-hidden="true" role="img" viewBox="0 0 448 512" style="height:1em;width:0.88em;vertical-align:-0.125em;margin-left:auto;margin-right:0.2em;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M160 80c0-26.5 21.5-48 48-48h32c26.5 0 48 21.5 48 48V432c0 26.5-21.5 48-48 48H208c-26.5 0-48-21.5-48-48V80zM0 272c0-26.5 21.5-48 48-48H80c26.5 0 48 21.5 48 48V432c0 26.5-21.5 48-48 48H48c-26.5 0-48-21.5-48-48V272zM368 96h32c26.5 0 48 21.5 48 48V432c0 26.5-21.5 48-48 48H368c-26.5 0-48-21.5-48-48V144c0-26.5 21.5-48 48-48z"/></svg> **An advanced stats mistake:** Modelling categorical, ordinal data as if it were numeric. ] .pull-right[ Apply and interpret ordinal regression models (e.g., `polr()` from `MASS`). ] <!-- --> .pull-left[ <svg aria-hidden="true" role="img" viewBox="0 0 384 512" style="height:1em;width:0.75em;vertical-align:-0.125em;margin-left:auto;margin-right:0.2em;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M192 32c17.7 0 32 14.3 32 32V199.5l111.5-66.9c15.2-9.1 34.8-4.2 43.9 11s4.2 34.8-11 43.9L254.2 256l114.3 68.6c15.2 9.1 20.1 28.7 11 43.9s-28.7 20.1-43.9 11L224 312.5V448c0 17.7-14.3 32-32 32s-32-14.3-32-32V312.5L48.5 379.4c-15.2 9.1-34.8 4.2-43.9-11s-4.2-34.8 11-43.9L129.8 256 15.5 187.4c-15.2-9.1-20.1-28.7-11-43.9s28.7-20.1 43.9-11L160 199.5V64c0-17.7 14.3-32 32-32z"/></svg> **A foundational stats mistake:** Interpreting a significant *p*-value as evidence that an effect exists in the real world. ] .pull-right[ ] --- ### Are the effects of `gender` significant? ``` ## Coefficients: ## Value Std. Error t value ## genderMale/Man -0.3280 0.03015 -10.880 ## genderAnother Gender 0.4846 0.11992 4.041 ``` No *p*-values in the model summary. -- But it’s common practice to compare these *t*-values to a standard normal distribution. -- <img src="data:image/png;base64,#demo_files/figure-html/zscore-mm-1.png" width="648" style="display: block; margin: auto;" /> -- <img src="data:image/png;base64,#demo_files/figure-html/zscore-ag-1.png" width="648" style="display: block; margin: auto;" /> ??? Since both *p*-values are below 0.05: - we CAN reject the null hypothesis that gender has no effect on ratings. - **we CANNOT conclude that there really is an effect of gender.** --- ### Why don’t significant *p*-values mean an effect exists? -- Because we can also get significant *p*-values when there really is *no* effect. -- .pull-left[ No difference in the true population: <img src="data:image/png;base64,#demo_files/figure-html/true-skew-probdist-1.png" width="504" style="display: block; margin: auto;" /> ] -- .pull-right[ A possible random sample (*n* = 50 per group): <img src="data:image/png;base64,#demo_files/figure-html/simdat-1.png" width="504" style="display: block; margin: auto;" /> ] ??? Not only the case for ordinal models. True across the board. But I'll illustrate it for present purposes using an ordinal model. --- ### Why don’t significant *p*-values mean an effect exists? ``` r sim_fit <- polr(rating ~ group, data = simdat, method = 'probit', Hess = TRUE) summary(sim_fit) ``` ``` ## Coefficients: ## Value Std. Error t value ## groupGroup B -0.4479 0.2229 -2.009 ``` <br> -- <img src="data:image/png;base64,#demo_files/figure-html/zscore-sim-1.png" width="648" style="display: block; margin: auto;" /> <br> So *p* is significant, but in the true population, Group A and Group B were identical! --- .pull-left[ ### The mistake ] .pull-right[ ### How you’ll avoid it ] <!-- --> .pull-left[ <svg aria-hidden="true" role="img" viewBox="0 0 640 512" style="height:1em;width:1.25em;vertical-align:-0.125em;margin-left:auto;margin-right:0.2em;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M392.8 1.2c-17-4.9-34.7 5-39.6 22l-128 448c-4.9 17 5 34.7 22 39.6s34.7-5 39.6-22l128-448c4.9-17-5-34.7-22-39.6zm80.6 120.1c-12.5 12.5-12.5 32.8 0 45.3L562.7 256l-89.4 89.4c-12.5 12.5-12.5 32.8 0 45.3s32.8 12.5 45.3 0l112-112c12.5-12.5 12.5-32.8 0-45.3l-112-112c-12.5-12.5-32.8-12.5-45.3 0zm-306.7 0c-12.5-12.5-32.8-12.5-45.3 0l-112 112c-12.5 12.5-12.5 32.8 0 45.3l112 112c12.5 12.5 32.8 12.5 45.3 0s12.5-32.8 0-45.3L77.3 256l89.4-89.4c12.5-12.5 12.5-32.8 0-45.3z"/></svg> **A common R mistake:** Letting R treat all variables that look like numbers as continuous numeric. ] .pull-right[ When a variable comes from a Likert scale, tell R it’s categorical using `factor()`. ] <!-- --> .pull-left[ <svg aria-hidden="true" role="img" viewBox="0 0 448 512" style="height:1em;width:0.88em;vertical-align:-0.125em;margin-left:auto;margin-right:0.2em;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M160 80c0-26.5 21.5-48 48-48h32c26.5 0 48 21.5 48 48V432c0 26.5-21.5 48-48 48H208c-26.5 0-48-21.5-48-48V80zM0 272c0-26.5 21.5-48 48-48H80c26.5 0 48 21.5 48 48V432c0 26.5-21.5 48-48 48H48c-26.5 0-48-21.5-48-48V272zM368 96h32c26.5 0 48 21.5 48 48V432c0 26.5-21.5 48-48 48H368c-26.5 0-48-21.5-48-48V144c0-26.5 21.5-48 48-48z"/></svg> **An advanced stats mistake:** Modelling categorical, ordinal data as if it were numeric. ] .pull-right[ Apply and interpret ordinal regression models (e.g., `polr()` from `MASS`). ] <!-- --> .pull-left[ <svg aria-hidden="true" role="img" viewBox="0 0 384 512" style="height:1em;width:0.75em;vertical-align:-0.125em;margin-left:auto;margin-right:0.2em;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M192 32c17.7 0 32 14.3 32 32V199.5l111.5-66.9c15.2-9.1 34.8-4.2 43.9 11s4.2 34.8-11 43.9L254.2 256l114.3 68.6c15.2 9.1 20.1 28.7 11 43.9s-28.7 20.1-43.9 11L224 312.5V448c0 17.7-14.3 32-32 32s-32-14.3-32-32V312.5L48.5 379.4c-15.2 9.1-34.8 4.2-43.9-11s-4.2-34.8 11-43.9L129.8 256 15.5 187.4c-15.2-9.1-20.1-28.7-11-43.9s28.7-20.1 43.9-11L160 199.5V64c0-17.7 14.3-32 32-32z"/></svg> **A foundational stats mistake:** Interpreting a significant *p*-value as evidence that an effect exists in the real world. ] .pull-right[ ] --- count:false .pull-left[ ### The mistake ] .pull-right[ ### How you’ll avoid it ] <!-- --> .pull-left[ <svg aria-hidden="true" role="img" viewBox="0 0 640 512" style="height:1em;width:1.25em;vertical-align:-0.125em;margin-left:auto;margin-right:0.2em;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M392.8 1.2c-17-4.9-34.7 5-39.6 22l-128 448c-4.9 17 5 34.7 22 39.6s34.7-5 39.6-22l128-448c4.9-17-5-34.7-22-39.6zm80.6 120.1c-12.5 12.5-12.5 32.8 0 45.3L562.7 256l-89.4 89.4c-12.5 12.5-12.5 32.8 0 45.3s32.8 12.5 45.3 0l112-112c12.5-12.5 12.5-32.8 0-45.3l-112-112c-12.5-12.5-32.8-12.5-45.3 0zm-306.7 0c-12.5-12.5-32.8-12.5-45.3 0l-112 112c-12.5 12.5-12.5 32.8 0 45.3l112 112c12.5 12.5 32.8 12.5 45.3 0s12.5-32.8 0-45.3L77.3 256l89.4-89.4c12.5-12.5 12.5-32.8 0-45.3z"/></svg> **A common R mistake:** Letting R treat all variables that look like numbers as continuous numeric. ] .pull-right[ When a variable comes from a Likert scale, tell R it’s categorical using `factor()`. ] <!-- --> .pull-left[ <svg aria-hidden="true" role="img" viewBox="0 0 448 512" style="height:1em;width:0.88em;vertical-align:-0.125em;margin-left:auto;margin-right:0.2em;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M160 80c0-26.5 21.5-48 48-48h32c26.5 0 48 21.5 48 48V432c0 26.5-21.5 48-48 48H208c-26.5 0-48-21.5-48-48V80zM0 272c0-26.5 21.5-48 48-48H80c26.5 0 48 21.5 48 48V432c0 26.5-21.5 48-48 48H48c-26.5 0-48-21.5-48-48V272zM368 96h32c26.5 0 48 21.5 48 48V432c0 26.5-21.5 48-48 48H368c-26.5 0-48-21.5-48-48V144c0-26.5 21.5-48 48-48z"/></svg> **An advanced stats mistake:** Modelling categorical, ordinal data as if it were numeric. ] .pull-right[ Apply and interpret ordinal regression models (e.g., `polr()` from `MASS`). ] <!-- --> .pull-left[ <svg aria-hidden="true" role="img" viewBox="0 0 384 512" style="height:1em;width:0.75em;vertical-align:-0.125em;margin-left:auto;margin-right:0.2em;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M192 32c17.7 0 32 14.3 32 32V199.5l111.5-66.9c15.2-9.1 34.8-4.2 43.9 11s4.2 34.8-11 43.9L254.2 256l114.3 68.6c15.2 9.1 20.1 28.7 11 43.9s-28.7 20.1-43.9 11L224 312.5V448c0 17.7-14.3 32-32 32s-32-14.3-32-32V312.5L48.5 379.4c-15.2 9.1-34.8 4.2-43.9-11s-4.2-34.8 11-43.9L129.8 256 15.5 187.4c-15.2-9.1-20.1-28.7-11-43.9s28.7-20.1 43.9-11L160 199.5V64c0-17.7 14.3-32 32-32z"/></svg> **A foundational stats mistake:** Interpreting a significant *p*-value as evidence that an effect exists in the real world. ] .pull-right[ Understand that significant *p*-values can arise even if no effect exists in the real world. ] --- <img src="data:image/png;base64,#demo_files/figure-html/anx-normal-end1-1.png" width="864" style="display: block; margin: auto;" /> --- count:false <img src="data:image/png;base64,#demo_files/figure-html/anx-normal-end2-1.png" width="864" style="display: block; margin: auto;" /> --- .pull-left[ ### The mistake ] .pull-right[ ### How you’ll avoid it ] <!-- --> .pull-left[ <svg aria-hidden="true" role="img" viewBox="0 0 640 512" style="height:1em;width:1.25em;vertical-align:-0.125em;margin-left:auto;margin-right:0.2em;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M392.8 1.2c-17-4.9-34.7 5-39.6 22l-128 448c-4.9 17 5 34.7 22 39.6s34.7-5 39.6-22l128-448c4.9-17-5-34.7-22-39.6zm80.6 120.1c-12.5 12.5-12.5 32.8 0 45.3L562.7 256l-89.4 89.4c-12.5 12.5-12.5 32.8 0 45.3s32.8 12.5 45.3 0l112-112c12.5-12.5 12.5-32.8 0-45.3l-112-112c-12.5-12.5-32.8-12.5-45.3 0zm-306.7 0c-12.5-12.5-32.8-12.5-45.3 0l-112 112c-12.5 12.5-12.5 32.8 0 45.3l112 112c12.5 12.5 32.8 12.5 45.3 0s12.5-32.8 0-45.3L77.3 256l89.4-89.4c12.5-12.5 12.5-32.8 0-45.3z"/></svg> **A common R mistake:** Letting R treat all variables that look like numbers as continuous numeric. ] .pull-right[ When a variable comes from a Likert scale, tell R it’s categorical using `factor()`. ] <!-- --> .pull-left[ <svg aria-hidden="true" role="img" viewBox="0 0 448 512" style="height:1em;width:0.88em;vertical-align:-0.125em;margin-left:auto;margin-right:0.2em;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M160 80c0-26.5 21.5-48 48-48h32c26.5 0 48 21.5 48 48V432c0 26.5-21.5 48-48 48H208c-26.5 0-48-21.5-48-48V80zM0 272c0-26.5 21.5-48 48-48H80c26.5 0 48 21.5 48 48V432c0 26.5-21.5 48-48 48H48c-26.5 0-48-21.5-48-48V272zM368 96h32c26.5 0 48 21.5 48 48V432c0 26.5-21.5 48-48 48H368c-26.5 0-48-21.5-48-48V144c0-26.5 21.5-48 48-48z"/></svg> **An advanced stats mistake:** Modelling categorical, ordinal data as if it were numeric. ] .pull-right[ Apply and interpret ordinal regression models (e.g., `polr()` from `MASS`). ] <!-- --> .pull-left[ <svg aria-hidden="true" role="img" viewBox="0 0 384 512" style="height:1em;width:0.75em;vertical-align:-0.125em;margin-left:auto;margin-right:0.2em;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M192 32c17.7 0 32 14.3 32 32V199.5l111.5-66.9c15.2-9.1 34.8-4.2 43.9 11s4.2 34.8-11 43.9L254.2 256l114.3 68.6c15.2 9.1 20.1 28.7 11 43.9s-28.7 20.1-43.9 11L224 312.5V448c0 17.7-14.3 32-32 32s-32-14.3-32-32V312.5L48.5 379.4c-15.2 9.1-34.8 4.2-43.9-11s-4.2-34.8 11-43.9L129.8 256 15.5 187.4c-15.2-9.1-20.1-28.7-11-43.9s28.7-20.1 43.9-11L160 199.5V64c0-17.7 14.3-32 32-32z"/></svg> **A foundational stats mistake:** Interpreting a significant *p*-value as evidence that an effect exists in the real world. ] .pull-right[ Understand that significant *p*-values can arise even if no effect exists in the real world. ] -- <br> .center[**Thank you! <svg aria-hidden="true" role="img" viewBox="0 0 512 512" style="height:1em;width:1em;vertical-align:-0.125em;margin-left:0.2em;margin-right:0.2em;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M464 256A208 208 0 1 0 48 256a208 208 0 1 0 416 0zM0 256a256 256 0 1 1 512 0A256 256 0 1 1 0 256zm177.6 62.1C192.8 334.5 218.8 352 256 352s63.2-17.5 78.4-33.9c9-9.7 24.2-10.4 33.9-1.4s10.4 24.2 1.4 33.9c-22 23.8-60 49.4-113.6 49.4s-91.7-25.5-113.6-49.4c-9-9.7-8.4-24.9 1.4-33.9s24.9-8.4 33.9 1.4zm40-89.3l0 0 0 0-.2-.2c-.2-.2-.4-.5-.7-.9c-.6-.8-1.6-2-2.8-3.4c-2.5-2.8-6-6.6-10.2-10.3c-8.8-7.8-18.8-14-27.7-14s-18.9 6.2-27.7 14c-4.2 3.7-7.7 7.5-10.2 10.3c-1.2 1.4-2.2 2.6-2.8 3.4c-.3 .4-.6 .7-.7 .9l-.2 .2 0 0 0 0 0 0c-2.1 2.8-5.7 3.9-8.9 2.8s-5.5-4.1-5.5-7.6c0-17.9 6.7-35.6 16.6-48.8c9.8-13 23.9-23.2 39.4-23.2s29.6 10.2 39.4 23.2c9.9 13.2 16.6 30.9 16.6 48.8c0 3.4-2.2 6.5-5.5 7.6s-6.9 0-8.9-2.8l0 0 0 0zm160 0l0 0-.2-.2c-.2-.2-.4-.5-.7-.9c-.6-.8-1.6-2-2.8-3.4c-2.5-2.8-6-6.6-10.2-10.3c-8.8-7.8-18.8-14-27.7-14s-18.9 6.2-27.7 14c-4.2 3.7-7.7 7.5-10.2 10.3c-1.2 1.4-2.2 2.6-2.8 3.4c-.3 .4-.6 .7-.7 .9l-.2 .2 0 0 0 0 0 0c-2.1 2.8-5.7 3.9-8.9 2.8s-5.5-4.1-5.5-7.6c0-17.9 6.7-35.6 16.6-48.8c9.8-13 23.9-23.2 39.4-23.2s29.6 10.2 39.4 23.2c9.9 13.2 16.6 30.9 16.6 48.8c0 3.4-2.2 6.5-5.5 7.6s-6.9 0-8.9-2.8l0 0 0 0 0 0z"/></svg> Time for questions!**] --- count: false ### Some really nice resources - Jamieson's (2004) paper **[Likert scales: How to (ab)use them.](https://onlinelibrary.wiley.com/doi/10.1111/j.1365-2929.2004.02012.x)** - UCLA Statistical Methods and Data Analytics's web page **[Ordinal Logistic Regression.](https://stats.oarc.ucla.edu/r/dae/ordinal-logistic-regression/)** - Kurz' (2021) blog post **[Notes on the Bayesian cumulative probit.](https://stats.oarc.ucla.edu/r/dae/ordinal-logistic-regression/)** - Vasishth and Nicenboim's (2016) paper **[Statistical Methods for Linguistic Research: Foundational Ideas – Part I.](https://doi.org/10.1111/lnc3.12201)** - Gelman and Hill's (2007) book **[Data Analysis Using Regression and Multilevel/Hierarchical Models.](https://www.cambridge.org/highereducation/books/data-analysis-using-regression-and-multilevel-hierarchical-models/32A29531C7FD730C3A68951A17C9D983)** --- count: false ### Plot on Slide 2 from Reeder, P. A., Newport, E. L., & Aslin, R. N. (2017). Distributional learning of subcategories in an artificial grammar: Category generalization and subcategory restrictions. *Journal of Memory and Language*, 97, 17–29. ### Data from Terry, J., Ross, R. M., Nagy, T., Salgado, M., Garrido-Vásquez, P., Sarfo, J. O., Cooper, S., Buttner, A. C., Lima, T. J. S., Öztürk, İ., Akay, N., Santos, F. H., Artemenko, C., Copping, L. T., Elsherif, M. M., Milovanović, I., Cribbie, R. A., Drushlyak, M. G., Swainston, K., … Field, A. P. (2023). Data from an International Multi-Centre Study of Statistics and Mathematics Anxieties and Related Variables in University Students (the SMARVUS Dataset). *Journal of Open Psychology Data*, 11(1), 8.